About PGCM (DSFM) Programme
Data has emerged as a new functional requirement in modern-age decision making. All organizations treat data as an input to arrive at any decision-making pertaining to their products and services. The Post Graduate Certificate in Management (Data Science in Financial Markets), offered by the National Institute of Securities Markets, is a premier programme which provides the blended knowledge of modern finance and its various application in collaboration with data processing techniques and tools to make, alter, various financial products, to make them more suitable for each class of investors. It offers a unique opportunity to learn and make use of the available financial data to make informed business decisions, keeping in mind the profitability and customer satisfaction. PGCM (DSFM) is a 14-month regular weekend programme, hosted at NISM’s facility in Bandra Kurla Complex, Mumbai, and is approved by the All India Council of Technical Education.
For Whom?
PGCM (DSFM) is ideal for Individuals with a passion for financial markets and keenness to acquire in-depth understanding of data mining techniques and financial analytics, to offer and innovate new-age financial products. The programme is suitable for both working professionals and fresh graduates.
Graduates from various disciplines, such as Commerce & Accounting, Management, Economics, Law, Mathematics, Statistics, Engineering, etc. will develop their analytical capabilities for financial markets.
What do I learn?
PGCM (DSFM) will enable professionals to undertake data mining with powerful tools to find trends and answer questions for businesses, researchers, non-profit organizations, academic institutions, and governments. The key takeaways from the programme are as follows:
- The programme will equip professionals with necessary financial knowledge and data mining skills to mould and design better and cost-efficient financial products, creation of efficient risk management system and systematic analysis of available financial information to make informed management decisions.
- The programme will provide a platform to its participants to learn the intricacies of Finance and modern age Data Science tools to develop the ability to support their decisions through analytic reasoning using variety of statistical and mathematical techniques.
- The curriculum covers topics such as Financial Products and Institutions, Programming language for Financial applications, Data Mining, Machine Learning, Algorithmic Trading, Big Data Processing and Visualization and combines academic elegance and business relevance to facilitate the participants to learn modalities of various financial products, followed by analytical techniques and weaves them with applications for data-based decision making.
- Dissemination of knowledge of various financial products to create level playing field between finance and non-finance professionals.
- An integral part of the learning experience is the use of Data Science and Analytics tools wherein the participants get hands-on exposure to R, Tableau, and Python.
- Lectures and hands-on sessions will be conducted by eminent faculty, industry leaders and experts.
Where does PGCM (DSFM) lead to?
PGCM (DSFM) could lead to the following job profiles, which involves the fusion of data science techniques and financial analytics:
- Credit Research and Ratings
- Investment Evaluation and Portfolio Management
- Risk Modelling
- Claim Processing
- Fraud Detection
- Analysing Financial Statements
- Equity Research
Programme Architecture
PGCM (DSFM) comprises of five trimesters having 16 courses and project / internship carrying a total of 45 credits. The curriculum is designed to provide research-based inputs and industry insights through a team of academicians and market experts. The detailed curriculum is as follows:
Subject Code |
Trimester I |
Hours |
Credits |
101 |
Mathematics |
30 |
3 |
102 |
Statistics |
30 |
3 |
103 |
Computer Programming |
30 |
3 |
|
|
|
|
Subject Code |
Trimester II |
Hours |
Credits |
201 |
Financial Economics |
15 |
1.5 |
202 |
Financial Institutions & Products |
15 |
1.5 |
203 |
Advanced Computer Programming |
30 |
3 |
204 |
Econometric and Time Series Analysis |
30 |
3 |
|
|
|
|
Subject Code |
Trimester III |
Hours |
Credits |
301 |
Fixed Income |
15 |
1.5 |
302 |
Financial Derivatives |
15 |
1.5 |
303 |
Market Microstructure and Algorithmic Trading |
30 |
3 |
304 |
Machine Learning and Deep Learning Models |
30 |
3 |
|
|
|
|
Subject Code |
Trimester IV |
Hours |
Credits |
401 |
Applications of Data Science in Securities Markets, Insurance Markets and Banking (Credit Risk) |
15 |
1.5 |
402 |
Financial Data and Management |
15 |
1.5 |
403 |
Security Analysis and Portfolio Management |
30 |
3 |
404 |
Big Data Processing and Visualization |
30 |
3 |
|
|
|
|
Subject Code |
Trimester V |
Hours |
Credits |
501 |
Financial Analytics |
30 |
3 |
502 |
Project/Internship |
|
6 |
Total Programme Credits |
45 |
The performance of participants is assessed on continuous evaluation process in each trimester. Evaluation is through quizzes, surprise tests, mid-term tests, end-term examinations, class participation, presentations, submissions, projects, etc., as may be indicated by the faculty members in the course outline. At the end of the programme, participants shall be awarded with Grades, subject-wise as well as the CGPA. The PGCM (DSFM) Certification will be awarded to successful candidates at a convocation ceremony.
Schedule
The programme will take place in class-room mode on weekends at NISM’s facility in the Bandra Kurla Complex, Mumbai. The class timings would be as follows:
Day |
Timings |
Teaching Hours |
Break Time |
Saturday |
6 pm to 9:15 pm |
3 hours |
15 Minutes |
Sunday |
10 am to 5 pm |
6 hours |
|
Note:
Note: The classes may begin in online format (webinar-mode), given the COVID-19 pandemic situation. After the improvement in COVID-19 pandemic situation, classes will be held only in class-room mode, and students have to attend classes in class room mode. No exemption will be given to anyone from attending classes. Prospective students of the program are requested to make a note of the same.
Hostel facility is available, at NISM Campus in Patalganga, for the benefit of participants from outside Mumbai.
Admissions 2022
NISM is pleased to invite applications for admissions to Post Graduate Certificate in Management (Data Science in Financial Markets) 2022-23.
Eligibility
Graduates from any discipline with a minimum of 50% marks from a recognized University under 10+2+3 stream are eligible. Provisional admission will be granted to those in the final year of graduation and subject to confirmation of the aforesaid criterion.
Selection Criteria
Candidates will be selected based on their profile and their performance in the personal interview (online).
How to Apply?
1. New user has to register by clicking on “Click here to Apply Now” button.
2. Upon successful registration, a User-ID and Password will be sent to registered email ID and mobile number of the candidate.
3. Fill the Application form and upload all relevant documents.
4.Complete the application form by paying the application fee – Rs. 500/-
Important Dates for AY 2023-24
Particulars |
Date |
Start Date for Application |
To be announced |
Last Date for Application |
To be announced |
Date of commencement of interviews |
To be announced |
Declaration of Merit List: |
To be announced |
Commencement of Program: |
As per AICTE norms |
Admission related FAQs
FAQ
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Dr. Kirti Arekar
Professor - School for Securities Education (SSE)
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Name: |
Dr. Kirti Arekar |
Designation: |
Professor - School for Securities Education (SSE) |
Email: |
kirti.arekar@nism.ac.in |
Contact: |
02192-668369 |
Dr. Kirti Arekar is currently working with National Institute of Securities Markets as a Professor. She has been in teaching, research, training and consulting in Data and Statistical Analysis since 2000. Dr. Kirti worked with K.J. Somaiya Institute of Management, Welingkar Institute of Management (WeSchool) and Narsee Monjee Institute of Management Mumbai. She has been visiting faculty with SP Jain Global Institute of Management, Singapore and Manipal University, Dubai. She has conducted several training programs for corporates like BSE, NSE, BCCI, INS HAMLA, Lumiere, Mahindra & Mahindra and L&T etc. based on Decision Making, Statistics and Data Analysis etc. by using several Software’s i.e. SPSS, EXCEL, EXCEL Solver, MegaStat, Minitab, SAS, R, Python, SAS and QM3+ etc. She has 275 Research publications in International and National Journals. She has presented several Research Papers in many Universities like Harvard University, Manchester, University of Emirates, University of Wellington and Hull University etc.
She has done PhD. in Statistics in 2002 and Integrated Program in Business Analytics from IIM Indore. She has received an award for Best Research paper at Inter 3RD International conference on global independence and decision science, Administrative Staff College of India, Hyderabad, 2008; Best paper awarded by Global Economy and Finance Journal, Canada, 2013 & University of Boston, USA in 2012. Under her guidance two students was awarded Ph.D. degree.
She was awarded as the Best Teacher Award for Excellence in Research & Teaching from Higher Education Forum in 2013; has been also awarded Fellowship from World Business Institute, Melbourne, Australia in 2013 and Best Researcher Award in Higher Education from K.J. Somaiya Institute of Management, Mumbai and Best Faculty Award in Teaching in Excellence from Bombay Management Association, BMA in 2019.
Publications
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Dr. Dhiraj Jain
Professor - School for Securities Education (SSE)
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Name: |
Dr. Dhiraj Jain |
Designation: |
Professor - School for Securities Education (SSE) |
Email: |
dhiraj.jain@nism.ac.in |
Dr. Dhiraj Jain is a Ph. D in Management and a Fellow of the Insurance Institute of India. He has a corporate experience of 10 years and an academic experience spanning nearly two decades. His areas of interest include Quantitative Techniques, Business Statistics, Investment & Portfolio Management, Mutual Funds & Financial Derivatives. He has been a Corporate trainer for Equity markets and Mutual Funds for various Financial Institutions, Mutual Funds and Broking Houses and others. He has taken various sessions on Statistics, Research Methodology, Security Analysis and Portfolio Management and Financial Derivatives for management professionals at Wipro, Infosys, Tata Motors, Citius Tech and the like. He is an avid researcher and has a number of research papers, case studies and publications to his credit. His book Marketing Techniques for Financial Inclusion and Development is also indexed in the Scopus database. He has presented papers in various National and International conferences and had also received the best paper award in many of them. He has been a Ph. D guide for the past 8 years. 5 students have completed their Ph. D under his guidance.
Publications.
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Dr. Kapil Shrimal
Associate Professor – School for Securities Education (SSE)
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Name: |
Dr. Kapil Shrimal |
Designation: |
Associate Professor – School for Securities Education (SSE) |
Email: |
kapil.shrimal@nism.ac.in |
Contact: |
+91-9978938182 / 02192- 668356 |
Dr. Kapil Shrimal is Associate Professor at National Institute of Securities Markets; he is having more than 13 years of experience of academics and 1-year experience of corporate. He has started his career with ICICI Prudential LIC and worked with Institutes like Prestige Institute of Management and Research, Indore, Symbiosis University of Applied Science Indore, Marwadi University Rajkot, etc. His last assignment was with PIMR, Indore.
His main area of teaching and research is Financial Planning, Portfolio Management, Derivatives, Financial Modelling, Financial Management, etc. He has conducted many Investment Awareness Program (IAP’s) on behalf of SEBI, NSE and ICAI India.
Dr. Shrimal has received Best Faculty award by Dainik Bhaskar in the year 2012. He has also received Best PhD Thesis award by Jaipuria Institute on Management Indore in the year 2018. He has presented and published research papers in International and National reputes and published one book. Some of his research papers got Best Research Paper awards in International Conferences.
He is into the editorial board and reviewer member of International Journal of Accounting and Financial Management Research. He is also associated with various Universities as a member of Board of Studies, Academic Council and PhD examiner.
Dr. Shrimal has done his PhD in Management (Finance) form Mohanlal Sukhadia University, Udaipur. He has also cleared UGC-NET and RPSC-SET in Management in the year 2012. Has done his MBA (Finance) and BBA from Devi Ahiliya University, Indore He has cleared many NISM certificates, NCFM modules and other certificate courses.
Publications
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Dr. Narsimhulu Siddula
Assistant Professor - School for Securities Education (SSE)
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Name: |
Dr. Narsimhulu Siddula |
Designation: |
Assistant Professor - School for Securities Education (SSE) |
Email: |
narsimhulu.siddula@nism.ac.in |
Contact: |
91-2192668418 |
Dr. Narsimhulu Siddula is an Assistant Professor at the National Institute of Securities Markets (NISM) with more than 15 years of Teaching and Research experience. Before joining NISM, he has worked as an Assistant Professor in the Department of Finance & Accounting at ICFAI Business School (IBS), a constituent of ICFAI Foundation for Higher Education (IFHE), Deemed-to-be-University, Hyderabad, Institute of Public Enterprise (IPE), Hyderabad as an ICSSR – Doctoral (Teacher) Research Fellow, and at reputed Management Institutes and Colleges in Hyderabad.
He holds Master of Commerce (M.Com) and Master of Business Administration (MBA) in the area of Finance, and Ph.D in the area of Finance on “Commodity Derivatives: Effectiveness in Price Discovery and Risk Management” from Department of Commerce, Osmania University, Hyderabad. He has qualified UGC – NET in Management and Commerce as well. He is the recipient of Indian Council of Social Science Research (ICSSR) - Doctoral (Teacher) Research Fellowship from Institute of Public Enterprise (IPE), Hyderabad, India (2013-15).
His areas of research interests include – Commodity Markets, Capital Markets, Asset Pricing, Corporate Finance, and Derivatives and Risk Management. He has published research papers in journals indexed in ABDC, ABS, Scopus, and others. He has presented research papers in various international and national conferences, and also participated in several workshops, conferences, and Faculty Development Programmes (FDPs).
His teaching interest lies in various subjects and areas related to Financial Reporting and Analysis, Corporate Finance, Securities Analysis and Equity Valuation, Forex Markets and International Finance, Fundamental Analysis, Commodity Markets, and Financial Risk Management. He teaches in various Full-time Academic and Executive Programmes offered at NISM. Also, takes sessions in training programmes at NISM.
Publications
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Mr. Suneel Sarswat
Adjunct Faculty
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Name: |
Mr. Suneel Sarswat |
Designation: |
Adjunct Faculty |
Suneel Sarswat is an adjunct faculty at NISM. His current research area is applications of theoretical computer science in finance and regulations. He teaches advanced programming, machine learning, deep learning, computational finance, etc. He has more than 14 years of teaching experience. Before joining academics, he has worked for Bank of America as a research analyst for more than two years. He has conducted various workshops for working professionals in the area of compuatational finance and machine learning. He has trained officers from RBI, SEBI, Exchanges, Banks, and other financial institutions. He has presented his work in international conferences in theoretical computer science. Besides adjunct faculty at NISM, he is also a part-time consultant at Decimal Point Analytics.
He has completed his master’s degree in applied statistics from the Indian Institute of Technology, Mumbai in 2005. He also holds a master’s degree from Tata Institute of Fundamental Research (TIFR), Mumbai in theoretical computer science. Currently, he is completing his Ph.D. from TIFR in the area of applications of logic.